While most baggage screening frameworks involved supervised learning, researchers have also explored adversarial learning to screen contraband data as anomalies. Akçay et al. [1]}, among others, laid the foundation of unsupervised baggage threat detection by proposing GANomaly [1]}, an encoder-decoder-encoder network trained in an adversarial manner to recognize prohibited items within baggage X-ray scans. In another work, they proposed Skip-GANomaly [3]} which employs skip-connections in an encoder-decoder topology that not only gives better latent representations for detecting baggage threats but also reduces the overall computational complexity of GANomaly [1]}.
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